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Mastering the game of Go without human knowledge

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  1. De Moor, Bram J. & Gijsbrechts, Joren & Boute, Robert N., 2022. "Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management," European Journal of Operational Research, Elsevier, vol. 301(2), pages 535-545.
  2. Bo Hu & Jiaxi Li & Shuang Li & Jie Yang, 2019. "A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR," Energies, MDPI, vol. 12(19), pages 1-15, September.
  3. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
  4. Jin, Jiahuan & Cui, Tianxiang & Bai, Ruibin & Qu, Rong, 2024. "Container port truck dispatching optimization using Real2Sim based deep reinforcement learning," European Journal of Operational Research, Elsevier, vol. 315(1), pages 161-175.
  5. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
  6. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  7. O’Malley, Cormac & de Mars, Patrick & Badesa, Luis & Strbac, Goran, 2023. "Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation," Applied Energy, Elsevier, vol. 349(C).
  8. Jiaxi Liu & Shuyi Lin & Linwei Xin & Yidong Zhang, 2023. "AI vs. Human Buyers: A Study of Alibaba’s Inventory Replenishment System," Interfaces, INFORMS, vol. 53(5), pages 372-387, September.
  9. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
  10. Huang, Shengzhi & Huang, Yong & Bu, Yi & Luo, Zhuoran & Lu, Wei, 2023. "Disclosing the interactive mechanism behind scientists’ topic selection behavior from the perspective of the productivity and the impact," Journal of Informetrics, Elsevier, vol. 17(2).
  11. Jun Li & Wei Zhu & Jun Wang & Wenfei Li & Sheng Gong & Jian Zhang & Wei Wang, 2018. "RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-18, November.
  12. Morato, P.G. & Andriotis, C.P. & Papakonstantinou, K.G. & Rigo, P., 2023. "Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  13. Yuchen Zhang & Wei Yang, 2022. "Breakthrough invention and problem complexity: Evidence from a quasi‐experiment," Strategic Management Journal, Wiley Blackwell, vol. 43(12), pages 2510-2544, December.
  14. Michael Curry & Alexander Trott & Soham Phade & Yu Bai & Stephan Zheng, 2022. "Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning," Papers 2201.01163, arXiv.org, revised Feb 2022.
  15. Yuling Huang & Xiaoping Lu & Chujin Zhou & Yunlin Song, 2023. "DADE-DQN: Dual Action and Dual Environment Deep Q-Network for Enhancing Stock Trading Strategy," Mathematics, MDPI, vol. 11(17), pages 1-27, August.
  16. Cheng, Haoxin & Li, Haihong & Dai, Qionglin & Yang, Junzhong, 2023. "A deep reinforcement learning method to control chaos synchronization between two identical chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
  17. Yang Qu & Ming-Xi Wang, 2019. "The option pricing model based on time values: an application of the universal approximation theory on unbounded domains," Papers 1910.01490, arXiv.org, revised Apr 2021.
  18. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
  19. Lu Wang & Wenqing Ai & Tianhu Deng & Zuo‐Jun M. Shen & Changjing Hong, 2020. "Optimal production ramp‐up in the smartphone manufacturing industry," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 685-704, December.
  20. Keller, Alexander & Dahm, Ken, 2019. "Integral equations and machine learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 2-12.
  21. Gurtej Singh Saini & Oney Erge & Pradeepkumar Ashok & Eric van Oort, 2022. "Well Construction Action Planning and Automation through Finite-Horizon Sequential Decision-Making," Energies, MDPI, vol. 15(16), pages 1-28, August.
  22. Yoav Flato & Roi Harel & Aviv Tamar & Ran Nathan & Tsevi Beatus, 2024. "Revealing principles of autonomous thermal soaring in windy conditions using vulture-inspired deep reinforcement-learning," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  23. Wei, Wei & Feng, Xiangnan, 2023. "Graphical representation and hierarchical decomposition mechanism for vertex-cover solution space," Applied Mathematics and Computation, Elsevier, vol. 458(C).
  24. Xianhua Peng & Chenyin Gong & Xue Dong He, 2023. "Reinforcement Learning for Financial Index Tracking," Papers 2308.02820, arXiv.org, revised Nov 2024.
  25. Minkyu Shin & Jin Kim & Bas van Opheusden & Thomas L. Griffiths, 2023. "Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty," Papers 2303.07462, arXiv.org, revised Apr 2023.
  26. Yi Cheng & Chuzhi Zhao & Pradeep Neupane & Bradley Benjamin & Jiawei Wang & Tongsheng Zhang, 2023. "Applicability and Trend of the Artificial Intelligence (AI) on Bioenergy Research between 1991–2021: A Bibliometric Analysis," Energies, MDPI, vol. 16(3), pages 1-15, January.
  27. Wang, Xuan & Wang, Rui & Jin, Ming & Shu, Gequn & Tian, Hua & Pan, Jiaying, 2020. "Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
  28. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
  29. Tasos Papagiannis & Georgios Alexandridis & Andreas Stafylopatis, 2022. "Pruning Stochastic Game Trees Using Neural Networks for Reduced Action Space Approximation," Mathematics, MDPI, vol. 10(9), pages 1-16, May.
  30. Yanwei Jia & Xun Yu Zhou, 2021. "Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms," Papers 2111.11232, arXiv.org, revised Jul 2022.
  31. Jonas Hanetho, 2023. "Commodities Trading through Deep Policy Gradient Methods," Papers 2309.00630, arXiv.org.
  32. Nesma M Ashraf & Reham R Mostafa & Rasha H Sakr & M Z Rashad, 2021. "Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-24, June.
  33. Karwowski, Jan & Mańdziuk, Jacek, 2019. "A Monte Carlo Tree Search approach to finding efficient patrolling schemes on graphs," European Journal of Operational Research, Elsevier, vol. 277(1), pages 255-268.
  34. Yanning Ge & Tao Huang & Xiaoding Wang & Guolong Zheng & Xu Yang, 2024. "The Application of Residual Connection-Based State Normalization Method in GAIL," Mathematics, MDPI, vol. 12(2), pages 1-14, January.
  35. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
  36. Noman, Abdullah Al & Tasneem, Zinat & Sahed, Md. Fahad & Muyeen, S.M. & Das, Sajal K. & Alam, Firoz, 2022. "Towards next generation Savonius wind turbine: Artificial intelligence in blade design trends and framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
  37. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
  38. Ricardo S. Alonso & Inés Sittón-Candanedo & Roberto Casado-Vara & Javier Prieto & Juan M. Corchado, 2020. "Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture," Sustainability, MDPI, vol. 12(14), pages 1-23, July.
  39. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
  40. Ma, Cong & Nan, Shijing, 2024. "Dynamic graph reinforcement learning algorithm for portfolio management: A novel time–frequency correlated model," Finance Research Letters, Elsevier, vol. 63(C).
  41. Canhoto, Ana Isabel, 2021. "Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective," Journal of Business Research, Elsevier, vol. 131(C), pages 441-452.
  42. Peter Eastman & Jade Shi & Bharath Ramsundar & Vijay S Pande, 2018. "Solving the RNA design problem with reinforcement learning," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-15, June.
  43. Zhang, Xi & Wang, Qin & Bi, Xiaowen & Li, Donghong & Liu, Dong & Yu, Yuanjin & Tse, Chi Kong, 2024. "Mitigating cascading failure in power grids with deep reinforcement learning-based remedial actions," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
  44. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
  45. Zhang, Guangming & Zhang, Chao & Wang, Wei & Cao, Huan & Chen, Zhenyu & Niu, Yuguang, 2023. "Offline reinforcement learning control for electricity and heat coordination in a supercritical CHP unit," Energy, Elsevier, vol. 266(C).
  46. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
  47. Sichen Ding & Gaiyun Liu & Li Yin & Jianzhou Wang & Zhiwu Li, 2024. "Detection of Cyber-Attacks in a Discrete Event System Based on Deep Learning," Mathematics, MDPI, vol. 12(17), pages 1-21, August.
  48. Oleg Szehr, 2021. "Hedging of Financial Derivative Contracts via Monte Carlo Tree Search," Papers 2102.06274, arXiv.org, revised Apr 2021.
  49. Hoffmann, Christian Hugo, 2022. "Is AI intelligent? An assessment of artificial intelligence, 70 years after Turing," Technology in Society, Elsevier, vol. 68(C).
  50. Fernando Martinez-Plumed & Emilia Gomez Gutierrez & Jose Hernandez-Orallo, 2020. "AI Watch Assessing Technology Readiness Levels for Artificial Intelligence," JRC Research Reports JRC122014, Joint Research Centre.
  51. Zhaobin Mo & Xuan Di & Rongye Shi, 2023. "Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection," Games, MDPI, vol. 14(1), pages 1-13, January.
  52. Guan, Xiaoshu & Sun, Huabin & Hou, Rongrong & Xu, Yang & Bao, Yuequan & Li, Hui, 2023. "A deep reinforcement learning method for structural dominant failure modes searching based on self-play strategy," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
  53. Md. Zahid Alam & Syed Moudud-Ul-Huq & Md. Nazmus Sadekin & Mohamad Ghozali Hassan & Mohammad Morshedur Rahman, 2021. "Influence of Social Distancing Behavior and Cross-Cultural Motivation on Consumers’ Attitude to Using M-Payment Services," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
  54. Kenshi Abe & Yusuke Kaneko, 2020. "Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games," Papers 2007.02141, arXiv.org, revised Dec 2020.
  55. Xiaoxuan Pan & Zhide Lu & Weiting Wang & Ziyue Hua & Yifang Xu & Weikang Li & Weizhou Cai & Xuegang Li & Haiyan Wang & Yi-Pu Song & Chang-Ling Zou & Dong-Ling Deng & Luyan Sun, 2023. "Deep quantum neural networks on a superconducting processor," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
  56. Minkyu Shin & Jin Kim & Minkyung Kim, 2020. "Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo," Papers 2012.15035, arXiv.org, revised Jan 2021.
  57. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
  58. Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021. "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers 2111.05188, arXiv.org.
  59. Fernando Fernandes Neto, 2018. "Generative Models for Stochastic Processes Using Convolutional Neural Networks," Papers 1801.03523, arXiv.org.
  60. Yuchao Dong, 2022. "Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm," Papers 2208.02409, arXiv.org, revised Sep 2023.
  61. Shijun Wang & Baocheng Zhu & Chen Li & Mingzhe Wu & James Zhang & Wei Chu & Yuan Qi, 2020. "Riemannian Proximal Policy Optimization," Computer and Information Science, Canadian Center of Science and Education, vol. 13(3), pages 1-93, August.
  62. Yanfei Kang & Rob J Hyndman & Feng Li, 2018. "Efficient generation of time series with diverse and controllable characteristics," Monash Econometrics and Business Statistics Working Papers 15/18, Monash University, Department of Econometrics and Business Statistics.
  63. Ma, Tao & Yang, Xuzhi & Szabo, Zoltan, 2024. "To switch or not to switch? Balanced policy switching in offline reinforcement learning," LSE Research Online Documents on Economics 124144, London School of Economics and Political Science, LSE Library.
  64. Ostheimer, Julia & Chowdhury, Soumitra & Iqbal, Sarfraz, 2021. "An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles," Technology in Society, Elsevier, vol. 66(C).
  65. Shohei Ohsawa, 2021. "Truthful Self-Play," Papers 2106.03007, arXiv.org, revised Feb 2023.
  66. Bünning, Felix & Huber, Benjamin & Schalbetter, Adrian & Aboudonia, Ahmed & Hudoba de Badyn, Mathias & Heer, Philipp & Smith, Roy S. & Lygeros, John, 2022. "Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC," Applied Energy, Elsevier, vol. 310(C).
  67. Xiaoyu Zhang & Rongheng Lin & Yuchang Bo & Fangchun Yang, 2022. "The Synergy of Double Neural Networks for Bridge Bidding," Mathematics, MDPI, vol. 10(17), pages 1-17, September.
  68. Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.
  69. Li, Jie & Wu, Xiaodong & Xu, Min & Liu, Yonggang, 2022. "Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections," Energy, Elsevier, vol. 251(C).
  70. Qu, Xiaobo & Yu, Yang & Zhou, Mofan & Lin, Chin-Teng & Wang, Xiangyu, 2020. "Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach," Applied Energy, Elsevier, vol. 257(C).
  71. Zhenchong Mo & Lin Gong & Mingren Zhu & Junde Lan, 2024. "The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning," Sustainability, MDPI, vol. 16(22), pages 1-34, November.
  72. Justin J. Boutilier & Timothy C. Y. Chan, 2023. "Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 64-83, January.
  73. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
  74. Zhang, Qin & Liu, Yu & Xiang, Yisha & Xiahou, Tangfan, 2024. "Reinforcement learning in reliability and maintenance optimization: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
  75. Yuxia Cheng & Zhiwei Wu & Kui Liu & Qing Wu & Yu Wang, 2019. "Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method," Sustainability, MDPI, vol. 11(7), pages 1-16, March.
  76. David E. Melnikoff & Ryan W. Carlson & Paul E. Stillman, 2022. "A computational theory of the subjective experience of flow," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  77. Alfonso-Sánchez, Sherly & Solano, Jesús & Correa-Bahnsen, Alejandro & Sendova, Kristina P. & Bravo, Cristián, 2024. "Optimizing credit limit adjustments under adversarial goals using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 315(2), pages 802-817.
  78. Cui, Tianxiang & Du, Nanjiang & Yang, Xiaoying & Ding, Shusheng, 2024. "Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
  79. Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 61-87, National Bureau of Economic Research, Inc.
  80. Takuya Ito & Guangyu Robert Yang & Patryk Laurent & Douglas H. Schultz & Michael W. Cole, 2022. "Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
  81. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
  82. Rachid Oucheikh & Tuwe Löfström & Ernst Ahlberg & Lars Carlsson, 2021. "Rolling Cargo Management Using a Deep Reinforcement Learning Approach," Logistics, MDPI, vol. 5(1), pages 1-18, February.
  83. Haoran Wang & Shi Yu, 2021. "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning," Papers 2105.09264, arXiv.org.
  84. Jan Groeneveld & Judith Herrmann & Nikkel Mollenhauer & Leonard Dreeßen & Nick Bessin & Johann Schulze Tast & Alexander Kastius & Johannes Huegle & Rainer Schlosser, 2024. "Self-learning Agents for Recommerce Markets," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(4), pages 441-463, August.
  85. Jan Balaguer & Raphael Koster & Ari Weinstein & Lucy Campbell-Gillingham & Christopher Summerfield & Matthew Botvinick & Andrea Tacchetti, 2022. "HCMD-zero: Learning Value Aligned Mechanisms from Data," Papers 2202.10122, arXiv.org, revised May 2022.
  86. U. Ayesta, 2022. "Reinforcement learning in queues," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 497-499, April.
  87. Bryan Lim & Stefan Zohren & Stephen Roberts, 2019. "Enhancing Time Series Momentum Strategies Using Deep Neural Networks," Papers 1904.04912, arXiv.org, revised Sep 2020.
  88. Zhang, Qingang & Zeng, Wei & Lin, Qinjie & Chng, Chin-Boon & Chui, Chee-Kong & Lee, Poh-Seng, 2023. "Deep reinforcement learning towards real-world dynamic thermal management of data centers," Applied Energy, Elsevier, vol. 333(C).
  89. Huy Chau & Duy Nguyen & Thai Nguyen, 2024. "Continuous-time optimal investment with portfolio constraints: a reinforcement learning approach," Papers 2412.10692, arXiv.org.
  90. Ayman Chaouki & Stephen Hardiman & Christian Schmidt & Emmanuel S'eri'e & Joachim de Lataillade, 2020. "Deep Deterministic Portfolio Optimization," Papers 2003.06497, arXiv.org, revised Apr 2020.
  91. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
  92. Zhou, Yuhao & Wang, Yanwei, 2022. "An integrated framework based on deep learning algorithm for optimizing thermochemical production in heavy oil reservoirs," Energy, Elsevier, vol. 253(C).
  93. Yang, Kaiyuan & Huang, Houjing & Vandans, Olafs & Murali, Adithya & Tian, Fujia & Yap, Roland H.C. & Dai, Liang, 2023. "Applying deep reinforcement learning to the HP model for protein structure prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
  94. Young Joon Park & Yoon Sang Cho & Seoung Bum Kim, 2019. "Multi-agent reinforcement learning with approximate model learning for competitive games," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-20, September.
  95. Mandal, Ankit & Tiwari, Yash & Panigrahi, Prasanta K. & Pal, Mayukha, 2022. "Physics aware analytics for accurate state prediction of dynamical systems," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
  96. Dong Liu & Feng Xiao & Jian Luo & Fan Yang, 2023. "Deep Reinforcement Learning-Based Holding Control for Bus Bunching under Stochastic Travel Time and Demand," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
  97. S. Bianchi & I. Muñoz-Martin & E. Covi & A. Bricalli & G. Piccolboni & A. Regev & G. Molas & J. F. Nodin & F. Andrieu & D. Ielmini, 2023. "A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  98. Adnan Jafar & Alessandra Kobayati & Michael A. Tsoukas & Ahmad Haidar, 2024. "Personalized insulin dosing using reinforcement learning for high-fat meals and aerobic exercises in type 1 diabetes: a proof-of-concept trial," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  99. MOTOHASHI Kazuyuki, 2020. "Science and Technology Co-evolution in AI: Empirical Understanding through a Linked Dataset of Scientific Articles and Patents," Discussion papers 20010, Research Institute of Economy, Trade and Industry (RIETI).
  100. Se-Hoon Jung & Jun-Ho Huh, 2019. "A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL," Sustainability, MDPI, vol. 11(13), pages 1-25, June.
  101. Jeong, Jaeik & Kim, Hongseok, 2021. "DeepComp: Deep reinforcement learning based renewable energy error compensable forecasting," Applied Energy, Elsevier, vol. 294(C).
  102. Bruno Gašperov & Stjepan Begušić & Petra Posedel Šimović & Zvonko Kostanjčar, 2021. "Reinforcement Learning Approaches to Optimal Market Making," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
  103. Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
  104. Lu, Chia-Hui, 2021. "The impact of artificial intelligence on economic growth and welfare," Journal of Macroeconomics, Elsevier, vol. 69(C).
  105. Christian Bayer & Benjamin Stemper, 2018. "Deep calibration of rough stochastic volatility models," Papers 1810.03399, arXiv.org.
  106. Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  107. Shui Jiang & Yanning Ge & Xu Yang & Wencheng Yang & Hui Cui, 2024. "UAV Control Method Combining Reptile Meta-Reinforcement Learning and Generative Adversarial Imitation Learning," Future Internet, MDPI, vol. 16(3), pages 1-18, March.
  108. Pablo Pedraza & Ian Vollbracht, 2023. "General theory of data, artificial intelligence and governance," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
  109. Yu, Haiyan & Yang, Ching-Chi & Yu, Ping, 2023. "Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1355-1364.
  110. Yifeng Guo & Xingyu Fu & Yuyan Shi & Mingwen Liu, 2018. "Robust Log-Optimal Strategy with Reinforcement Learning," Papers 1805.00205, arXiv.org.
  111. Lai, Jianfa & Weng, Lin-Chen & Peng, Xiaoling & Fang, Kai-Tai, 2022. "Construction of symmetric orthogonal designs with deep Q-network and orthogonal complementary design," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
  112. Xueqing Yan & Yongming Li, 2023. "A Novel Discrete Differential Evolution with Varying Variables for the Deficiency Number of Mahjong Hand," Mathematics, MDPI, vol. 11(9), pages 1-21, May.
  113. Hai Wang & Shengnan Chen, 2023. "Insights into the Application of Machine Learning in Reservoir Engineering: Current Developments and Future Trends," Energies, MDPI, vol. 16(3), pages 1-11, January.
  114. Marc Andreas Schmitt, 2022. "Deep Learning in Business Analytics: A Clash of Expectations and Reality," Papers 2205.09337, arXiv.org.
  115. Touzani, Samir & Prakash, Anand Krishnan & Wang, Zhe & Agarwal, Shreya & Pritoni, Marco & Kiran, Mariam & Brown, Richard & Granderson, Jessica, 2021. "Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency," Applied Energy, Elsevier, vol. 304(C).
  116. Michael S. Harré, 2022. "What Can Game Theory Tell Us about an AI ‘Theory of Mind’?," Games, MDPI, vol. 13(3), pages 1-11, June.
  117. Jermain C. Kaminski & Christian Hopp, 2020. "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, vol. 55(3), pages 627-649, October.
  118. Pujin Wang & Jianzhuang Xiao & Ken’ichi Kawaguchi & Lichen Wang, 2022. "Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
  119. Svetozarevic, B. & Baumann, C. & Muntwiler, S. & Di Natale, L. & Zeilinger, M.N. & Heer, P., 2022. "Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments," Applied Energy, Elsevier, vol. 307(C).
  120. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
  121. Thomas Lindner & Jonas Puck & Alain Verbeke, 2022. "Beyond addressing multicollinearity: Robust quantitative analysis and machine learning in international business research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(7), pages 1307-1314, September.
  122. Andrew J. Collins & Sheida Etemadidavan & Wael Khallouli, 2020. "Generating Empirical Core Size Distributions of Hedonic Games using a Monte Carlo Method," Papers 2007.12127, arXiv.org.
  123. José A. Torres-León & Marco A. Moreno-Armendáriz & Hiram Calvo, 2024. "Representing the Information of Multiplayer Online Battle Arena (MOBA) Video Games Using Convolutional Accordion Auto-Encoder (A 2 E) Enhanced by Attention Mechanisms," Mathematics, MDPI, vol. 12(17), pages 1-19, September.
  124. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
  125. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
  126. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  127. Gang Hu & Ming Gu, 2024. "Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management," Papers 2405.05449, arXiv.org.
  128. Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
  129. Olivier Gu'eant & Iuliia Manziuk, 2019. "Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality," Papers 1910.13205, arXiv.org.
  130. Dimitris Bertsimas & Bartolomeo Stellato, 2022. "Online Mixed-Integer Optimization in Milliseconds," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2229-2248, July.
  131. Malte Reinschmidt & József Fortágh & Andreas Günther & Valentin V. Volchkov, 2024. "Reinforcement learning in cold atom experiments," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  132. Wanyang Dai, 2024. "Stochastic Differential Games and a Unified Forward–Backward Coupled Stochastic Partial Differential Equation with Lévy Jumps," Mathematics, MDPI, vol. 12(18), pages 1-46, September.
  133. Bossert, Leonie & Hagendorff, Thilo, 2021. "Animals and AI. The role of animals in AI research and application – An overview and ethical evaluation," Technology in Society, Elsevier, vol. 67(C).
  134. Andrew E Levy & Minakshi Biswas & Rachel Weber & Khaldoun Tarakji & Mina Chung & Peter A Noseworthy & Christopher Newton-Cheh & Michael A Rosenberg, 2019. "Applications of machine learning in decision analysis for dose management for dofetilide," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-13, December.
  135. Andrew G. Haldane & Arthur E. Turrell, 2019. "Drawing on different disciplines: macroeconomic agent-based models," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 39-66, March.
  136. Yang, Zhengzhi & Zheng, Lei & Perc, Matjaž & Li, Yumeng, 2024. "Interaction state Q-learning promotes cooperation in the spatial prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 463(C).
  137. Eric Benhamou & David Saltiel & Jean-Jacques Ohana & Jamal Atif, 2020. "Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning," Papers 2009.07200, arXiv.org, revised Nov 2020.
  138. Bertschek, Irene & Arnold, Daniel & Erdsiek, Daniel & Nicolay, Katharina & Bieber, Daniel & Kreutzer, Elena, 2018. "Arbeiten 4.0 - Chancen und Herausforderungen für Luxemburg. Studie im Auftrag von Arbeitsministerium, Handelskammer, Arbeitnehmerkammer Luxemburg," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 181909, March.
  139. Andreas Kuhnle & Jan-Philipp Kaiser & Felix Theiß & Nicole Stricker & Gisela Lanza, 2021. "Designing an adaptive production control system using reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 855-876, March.
  140. Zoran Stoiljkovic, 2023. "Applying Reinforcement Learning to Option Pricing and Hedging," Papers 2310.04336, arXiv.org.
  141. Haoran Wang, 2019. "Large scale continuous-time mean-variance portfolio allocation via reinforcement learning," Papers 1907.11718, arXiv.org, revised Aug 2019.
  142. Lin Wang & Xingang Xu & Xuhui Zhao & Baozhu Li & Ruijuan Zheng & Qingtao Wu, 2021. "A randomized block policy gradient algorithm with differential privacy in Content Centric Networks," International Journal of Distributed Sensor Networks, , vol. 17(12), pages 15501477211, December.
  143. Bálint Kővári & Lászlo Szőke & Tamás Bécsi & Szilárd Aradi & Péter Gáspár, 2021. "Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission," Sustainability, MDPI, vol. 13(20), pages 1-18, October.
  144. Loris Cannelli & Giuseppe Nuti & Marzio Sala & Oleg Szehr, 2020. "Hedging using reinforcement learning: Contextual $k$-Armed Bandit versus $Q$-learning," Papers 2007.01623, arXiv.org, revised Feb 2022.
  145. R. A. Mashelkar, 2018. "Exponential Technology, Industry 4.0 and Future of Jobs in India," Review of Market Integration, India Development Foundation, vol. 10(2), pages 138-157, August.
  146. Zhao, Ying & Li, Liang & Lanteri, Stéphane & Viquerat, Jonathan, 2022. "Dynamic metasurface control using Deep Reinforcement Learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 377-395.
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